燕山大学学报2024，Vol.48Issue(6)：519-527,549,10.DOI:10.3969/j.issn.1007-791X.2024.06.006
基于大核分离和通道先验卷积注意的PCB缺陷检测方法
A PCB defect detection method based on large kernel separation and channel prior convolution attention
摘要
Abstract
Addressing the issues of small defect size,complex form,and low discriminability in printed circuit boards that lead to low detection accuracy and high false positive rates,a PCB defect detection method based on large kernel separation and channel prior convolutional attention is proposed.First,combining multi-scale feature extraction and spatial convolution attention mechanism,large kernel separation spatial pyramid pooling is proposed to enhance the multi-scale feature integration ability and modeling capability of the model.Second,the P2 small object detection layer is constructed in the neck network to enable the model to learn richer and more robust feature representations.The introduction of channel prior convolutional attention modules dynamically distributes attention weights along both the channel and spatial dimensions,retaining channel prior information while effectively extracting spatial relationships,thereby enhancing the detection accuracy of small object defects in the model.The experimental results indicate that the mAP of the proposed method on the PKU-Market-PCB dataset reached 98.6%,outperforming the baseline model YOLOv8n by 3.4%.The precision is improved by 2.6%,and the recall is increased by 4.6%.The inference time per image is only 4.1 ms,making it suitable for real-time detection.In summary,this method significantly enhances the accuracy and real-time performance of printed circuit board defect detection,providing high practical application value.关键词
缺陷检测/印刷电路板/YOLOv8/大核分离/注意力机制Key words
defect detection/printed circuit boards/YOLOv8/large kernel separation/attention mechanism分类
信息技术与安全科学引用本文复制引用
李扬,陈伟,杨清永,李现国,徐常余,徐晟..基于大核分离和通道先验卷积注意的PCB缺陷检测方法[J].燕山大学学报,2024,48(6):519-527,549,10.基金项目
国家自然科学基金资助项目(52271341) （52271341）
天津市科技计划项目(24YDTPJC00410) （24YDTPJC00410）
河北省高等学校科学技术研究项目(ZD2021037) （ZD2021037）
江苏省重点实验室对外开放课题资助项目(zdsys2019-11) （zdsys2019-11）
